Genetic and environmental implications of reintroducing laboratory-raised unionid mussels to the wild
Bibliographic record
Abstract
The reintroduction of endangered species is a potentially useful conservation strategy, which in the case of freshwater unionid mussels, must be preceded by the successful laboratory rearing of juvenile mussels on their host fishes. However, an understanding of the genetic and environmental implications of reintroductions of artificially propagated mussels is required. Unfortunately, there is a dearth of information on these issues with respect to freshwater mussels. In general, regarding the genetic effects of reintroductions, small founder populations may lead to low heterozygosity (reduced genetic variability) in the reintroduced populations, which can make them more susceptible to extinction. Captive breeding programs may also alter the genetic composition of species through artificial selection, whether intentional or unintentional. Captive breeding may also affect an individual’s interactions with conspecifics or predators by altering behaviour. Genetic problems in reintroduced populations also have the potential to affect wild populations, particularly by reducing variability among populations of the same species and eliminating local adaptation. There is also the possibility that diseases, parasites, or exotic species may be spread when populations are relocated or augmented. Recommendations related to the minimization of these impacts are presented for freshwater mussels, with the recognition that many of the issues will require additional study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".